ArticleScientific reports2025
A lightweight LSTM-based open-set RF fingerprinting identification for edge deployment.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- LUMEN: A Lightweight UAV Multi-Enhanced Network for PSD-Based RF Fingerprinting on Edge Devices.Sensors (Basel, Switzerland) · 2026Article
- High-precision prediction method for mine deformation based on GNSS RTK and stacking ensemble learning.Scientific reports · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the rapid advancement of deep learning techniques, numerous neural networks have been successfully developed for radio frequency (RF) fingerprinting identification. In this work, we propose a lightweight yet reliable neural network framework featuring a 9-layer architecture based on the long short-term memory (LSTM) strategy, designed for efficient open-set fingerprinting identification. The simulated beacon frames model real-world propagation effects by incorporating random modulation, power amplifier nonlinearity, multi-path fading, inherent radio noise, and additive channel noise. We extensively evaluate the identification accuracy and efficiency of our LSTM network identification against well-known deep learning models such as ResNet (144 layers) and GoogleNet (177 layers). The evaluation covers a wide range of parameters, including transmitter variability (s), number of transmitters (N), frames per transmitter ([Formula: see text]) and signal-to-noise ratio ([Formula: see text]). Our results show that the LSTM network maintains an accuracy of more than [Formula: see text] in [Formula: see text] with [Formula: see text], even with up to [Formula: see text] transmitters. At lower values ([Formula: see text], [Formula: see text] dB), our LSTM network outperforms GoogleNet and matches ResNet in accuracy. Furthermore, it achieves a training acceleration of up to [Formula: see text] for [Formula: see text] and [Formula: see text], with inference times under 2 seconds. Meanwhile here, the usage of VRAM is reduced by up to [Formula: see text], and the model disk size is under 1 MB. Experiments on devices, including a high performance computing (HPC) node, a personal computer (PC), and three smartphones, demonstrate that the optimal strategy depends on the scale of the problem: local processing for [Formula: see text], remote training with local inference for [Formula: see text], and full remote processing for [Formula: see text].
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.